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- W4288348493 abstract "How much can we infer about a person's looks from the way they speak? In this paper, we study the task of reconstructing a facial image of a person from a short audio recording of that person speaking. We design and train a deep neural network to perform this task using millions of natural Internet/YouTube videos of people speaking. During training, our model learns voice-face correlations that allow it to produce images that capture various physical attributes of the speakers such as age, gender and ethnicity. This is done in a self-supervised manner, by utilizing the natural co-occurrence of faces and speech in Internet videos, without the need to model attributes explicitly. We evaluate and numerically quantify how--and in what manner--our Speech2Face reconstructions, obtained directly from audio, resemble the true face images of the speakers." @default.
- W4288348493 created "2022-07-29" @default.
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- W4288348493 date "2019-05-23" @default.
- W4288348493 modified "2023-09-28" @default.
- W4288348493 title "Speech2Face: Learning the Face Behind a Voice" @default.
- W4288348493 doi "https://doi.org/10.48550/arxiv.1905.09773" @default.
- W4288348493 hasPublicationYear "2019" @default.
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